A texture analysis approach to supervised face segmentation

This paper proposes to segment face images into six classes (eyes, nose, mouth, hair/eyebrows/beard, skin, and background) by classifying pixels based on the texture features calculated in a neighborhood of each pixel. Leung-Malik filter banks are applied to the color images for feature extraction and Random Projections are used to reduce data dimensionality. In order to perform pixel classification, manually labeled images are used to train a Multi-Quadric Radial Basis Function Neural Network, with centers selected by the Fast Condensed Nearest Neighbor algorithm. Quantitative and qualitative results are presented and demonstrate that the methodology can correctly segment most of the class labels with high effectiveness rate, comparable with the results achieved by state-of-art methods.